• Language and Information
• /
• v.14 no.2
• /
• pp.159-181
• /
• 2010

This paper presents a hybrid approach to the sentiment analysis of online texts. The sentiment of a text refers to the feelings that the author of a text has towards a certain topic. Many existing approaches employ either a pattern-based approach or a machine learning based approach. The former shows relatively high precision in classifying the sentiments, but suffers from the data sparseness problem, i.e. the lack of patterns. The latter approach shows relatively lower precision, but 100% recall. The approach presented in the current work adopts the merits of both approaches. It combines the pattern-based approach with the machine learning based approach, so that the relatively high precision and high recall can be maintained. Our experiment shows that the hybrid approach improves the F-measure score for more than 50% in comparison with the pattern-based approach and for around 1% comparing with the machine learning based approach. The numerical improvement from the machine learning based approach might not seem to be quite encouraging, but the fact that in the current approach not only the sentiment or the polarity information of sentences but also the additional information such as target of sentiments can be classified makes the current approach promising.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.16 no.5
• /
• pp.622-626
• /
• 2006

In this paper, we show the usefulness of an methodology using a neural network that it analyzes a relation between learning personality related variables of the Enneargram and learning personality types. The Enneargram is a tool to classify learning personality types. In other words, we analyzed patterns of learning personality types-actaul-spontaneous type, actual-routine type, conceptual-specific type, conceptual-global type - by using the fuzzy TAM network that are very useful tool for pattern analysis.

• Journal of the Korea Society of Computer and Information
• /
• v.13 no.1
• /
• pp.243-251
• /
• 2008

The cyber classes of an e-Learning system have been considered as one of the important form of education. Especially. some of non-major(liberal arts and science) and major subjects are held in cyber classes. However, there is no or little study of effectiveness and function for the students' position. In this study, we analyzed log files in the e-learning system. and classified login and learning hour patterns of students. who were enrolled in the cyber classes. into hourly pattern in a day, daily pattern in a week, and weekly pattern in a semester. We proposed general ideas to improve effectiveness and function of current e-learning. Over 50% of logins were for less than 30 minutes learning and there is wasteful use of e-learning system resources.

• Journal of the Korea Society of Computer and Information
• /
• v.12 no.5
• /
• pp.171-177
• /
• 2007

The cyber classes of an e-Learning system have been considered as one of the important form of education. However, there is no or little study of effectiveness and function for the students' position. In this study, I analyzed log files in the e-learning system, and classified login and teaming hour patterns of students, who were enrolled in the undergraduate courses of the university, into hourly pattern in a daily pattern in a week, weekly pattern in a semester. I proposed general ideas to improve effectiveness and function of current e-learning. Over 50% of logins were for less than 30minutes learning and there is wasteful use of e-learning system resources.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.65 no.9
• /
• pp.1541-1550
• /
• 2016

In this paper, we introduce a novel learning methodology of fuzzy clustering-based neural network pattern classifier. Fuzzy clustering-based neural network pattern classifier depicts the patterns of given classes using fuzzy rules and categorizes the patterns on unseen data through fuzzy rules. Least squares estimator(LSE) or weighted least squares estimator(WLSE) is typically used in order to estimate the coefficients of polynomial function, but this study proposes a novel coefficient estimate method which includes advantages of the existing methods. The premise part of fuzzy rule depicts input space as "If" clause of fuzzy rule through fuzzy c-means(FCM) clustering, while the consequent part of fuzzy rule denotes output space through polynomial function such as linear, quadratic and their coefficients are estimated by the proposed local least squares estimator(LLSE)-based learning. In order to evaluate the performance of the proposed pattern classifier, the variety of machine learning data sets are exploited in experiments and through the comparative analysis of performance, it provides that the proposed LLSE-based learning method is preferable when compared with the other learning methods conventionally used in previous literature.

• The Journal of the Korea Contents Association
• /
• v.10 no.3
• /
• pp.463-471
• /
• 2010

The purpose of this study is to articulate learning model based on achievement level in blended learning environment. In order to investigate the variables and mechanisms in the blended learning environment, we started by attempt to develop two questionnaires using the components of web-based instruction and self-regulated learning. And its results were implemented to represent the topology and directed merging path within components. 154 students at a high school were required to take each web course respectively for two weeks. And questionnaires data, achievement levels data were collected and analyzed. Various statistical analysis methods such as correlation analysis, classical multidimensional scaling, multiple regression analysis, were applied to the data. As an result, the topology and directed path within factors of blended learning process were derived and revised as a final model.

• Journal of the Korean Chemical Society
• /
• v.36 no.1
• /
• pp.113-124
• /
• 1992

The pattern recognition methods of chemometrics have been applied to multivariate data, for which ninety four Korean ancient glass pieces have been determined for 12 elements by neutron activation analysis. For the purpose, principal component analysis and non-linear mapping have been used as the unsupervised learning methods. As the result, the glass samples have been classified into 6 classes. The SIMCA (statistical isolinear multiple component analysis), adopted as a supervised learning method, has been applied to the 6 training set and the test set. The results of the 6 training set were in accord with the results by principal component analysis and non-linear mapping. For test set, 17 of 33 samples were each allocated to one of the 6 training set.

• Fashion & Textile Research Journal
• /
• v.18 no.6
• /
• pp.766-775
• /
• 2016

In the era of globalization and unlimited competition, Korean universities need a breakthrough in their education system according to the changing education landscape, such as lower graduation requirements to cultivate more multi-talented convergence leaders. While each student has different learning capabilities, which results in different performance and achievements in the same class, the uniform education that most universities are currently offering fails to accommodate such differences. Blended learning, synergically combining offline and online classes, enlarges learning space and enriches learning experiences through diversified tools and materials, including multimedia. Recently, universities are increasingly adopting video contents and on-offline convergence learning strategy. Thus, this study suggests a teaching method based on blended learning to more effectively teach existing pattern CAD and virtual CAD in the Apparel Pattern CAD class. To this end, this researcher developed a teaching-learning method and curriculum according to the blended learning phase and video-based contents. The curriculum consisted of 2D CAD (SuperAlpha: Plus) and 3D CAD (CLO) software learning for 15 weeks. Then, it was loaded to the Learning Management System (LMS) and operated for 15 weeks both online and offline. The performance analysis of LMS usage found that class materials, among online postings, were viewed the most. The discussion menu most accurately depicted students' participation, and students who did not participate in discussions were estimated to check postings less than participating students. A survey on the blended learning found that students prefer digital or more digitized classes, while preferring face to face for Q&As.

• The Journal of Information Systems
• /
• v.27 no.3
• /
• pp.123-140
• /
• 2018

Purpose The purpose of this study is to explore the optimal trading frequency which is useful for stock price prediction by using deep learning for charting image data. We also want to identify the appropriate time for accurate forecasting of stock price when performing pattern analysis. Design/methodology/approach In order to find the optimal trading frequency patterns and forecast timings, this study is performed as follows. First, stock price data is collected using OpenAPI provided by Daishin Securities, and candle chart images are created by data frequency and forecasting time. Second, the patterns are generated by the charting images and the learning is performed using the CNN. Finally, we find the optimal trading frequency patterns and forecasting timings. Findings According to the experiment results, this study confirmed that when the 10 minute frequency data is judged to be a decline pattern at previous 1 tick, the accuracy of predicting the market frequency pattern at which the market decreasing is 76%, which is determined by the optimal frequency pattern. In addition, we confirmed that forecasting of the sales frequency pattern at previous 1 tick shows higher accuracy than previous 2 tick and 3 tick.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.23 no.1
• /
• pp.182-196
• /
• 2024

This study introduces driving pattern analysis and change detection methods using smartphone sensors, based on contrastive learning. These methods characterize driving patterns without labeled data, allowing accurate classification with minimal labeling. In addition, they are robust to domain changes, such as different vehicle types. The study also examined the applicability of these methods to smartphones by comparing them with six lightweight deep-learning models. This comparison supported the development of smartphone-based driving pattern analysis and assistance systems, utilizing smartphone sensors and contrastive learning to enhance driving safety and efficiency while reducing the need for extensive labeled data. This research offers a promising avenue for addressing contemporary transportation challenges and advancing intelligent transportation systems.